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The Development of Gamma Energy Identifying Algorithm for Compact Radiation Sensors Using Stepwise Refinement TechniqueThe Development of Gamma Energy Identifying Algorithm for Compact Radiation Sensors Using Stepwise Refinement Technique

Other Titles
The Development of Gamma Energy Identifying Algorithm for Compact Radiation Sensors Using Stepwise Refinement Technique
Authors
유현준김예원김현덕이윤조규성
Issue Date
2017
Publisher
대한방사선방어학회
Keywords
Energy identifying; Energy spectrum; Spectrum analysis; CsI(Tl) scintillator; Compact radiation sensor
Citation
방사선방어학회지, v.42, no.2, pp.91 - 97
Indexed
KCI
Journal Title
방사선방어학회지
Volume
42
Number
2
Start Page
91
End Page
97
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/85237
DOI
10.14407/jrpr.2017.42.2.91
ISSN
2508-1888
Abstract
Background: A gamma energy identifying algorithm using spectral decomposition combined with smoothing method was suggested to confirm the existence of the artificial radio isotopes. The algorithm is composed by original pattern recognition method and smoothing method to enhance the performance to identify gamma energy of radiation sensors that have low energy resolution. Materials and Methods: The gamma energy identifying algorithm for the compact radiation sensor is a three-step of refinement process. Firstly, the magnitude set is calculated by the original spectral decomposition. Secondly, the magnitude of modeling error in the magnitude set is reduced by the smoothing method. Thirdly, the expected gamma energy is finally decided based on the enhanced magnitude set as a result of the spectral decomposition with the smoothing method. The algorithm was optimized for the designed radiation sensor composed of a CsI (Tl) scintillator and a silicon pin diode. Results and Discussion: The two performance parameters used to estimate the algorithm are the accuracy of expected gamma energy and the number of repeated calculations. The original gamma energy was accurately identified with the single energy of gamma radiation by adapting this modeling error reduction method. Also the average error decreased by half with the multi energies of gamma radiation in comparison to the original spectral decomposition. In addition, the number of repeated calculations also decreased by half even in low fluence conditions under 10 4 (/0.09 cm 2 of the scintillator surface). Conclusion: Through the development of this algorithm, we have confirmed the possibility of developing a product that can identify artificial radionuclides nearby using inexpensive radiation sensors that are easy to use by the public. Therefore, it can contribute to reduce the anxiety of the public exposure by determining the presence of artificial radionuclides in the vicinity.
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